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What is data modeling?

In this article

  • Types of data modeling
  • Data modeling techniques
  • Data science and IT programs

Data modeling is the process of creating a simplified diagram of a software system and the data elements it contains. Data models can be used to categorize the various elements of data and regulate how these elements relate to each other and the properties of real-world systems. Accurate data modeling can help organizations maximize the value of their data for their unique needs.

A data model acts as a visual representation of data flow in and out of a database. Data models can be used to design new databases and improve the efficiency of older applications. Today, data modeling is utilized in a wide range of industries, including healthcare, finance, marketing and manufacturing.

Data modeling skills have become a valuable asset for job seekers in the IT field. There are several ways data modeling skills can benefit career development, and all aspiring IT professionals should understand certain things about data modeling. Read on to learn more!

Types of data modeling

There are three main types of data modeling. They are:

Conceptual data models 

Conceptual data models identify and describe the types of data contained in a system. Organizations use conceptual data modeling to identify important types of data entities for their operations and define the general relationship between each data entity. Teams of data scientists often collaborate with stakeholders and business specialists to develop conceptual data models.

Because of their simplicity and accessibility, conceptual models can be easily drawn on paper or a whiteboard. They are ideal for explaining data relationships to audiences who don’t have extensive tech knowledge. For example, a conceptual model can describe an organization’s important data types to shareholders and employees from outside the IT department.

Logical data models

Logical data models are similar to conceptual data models in structure but contain much more detail. While conceptual models broadly describe the data entities relevant to a business (like sales, customers and products), logical models define the key attributes for each entity like product IDs and store IDs. These details can provide users with greater insight regarding the relationships between data entities, which can help them determine the best way to implement systems.  

Like conceptual models, logical data models are database agnostic. They are not designed to function with any database management system (DBMS) and can be adapted to fit various DBMS structures. While they don’t contain all the information needed to design a database, logical models can help define key data elements and provide a solid foundation for creating physical models.

Physical data models 

While logical data models hypothetically describe how systems should be structured and implemented, physical models define how they will be implemented in practice. Unlike logical and conceptual models, physical models are database-specific, meaning they are designed to function with a particular DBMS and must be structured accordingly. 

Physical data models are organized into tables and columns rather than entities and attributes. The tables display additional information that pertains to data flow and storage, including the specific type of data in each column, constraints, primary keys, foreign keys and the relationships between tables. Physical data models are typically designed by data architects, as they must be detailed enough to guide the construction of the physical database.

Data modeling techniques

In data science, the type of model can determine the way a system organizes and stores data. There are three primary techniques used in data modeling, each with pros and cons.

Dimensional modeling

Dimensional data modeling employs a contextual data structure that is well suited for business use. Dimensional data models are organized into “fact tables” that contain the data for a specific type of activity, such as sales. Important categories of data, such as sales revenue, are labeled “facts” and grouped into tables.

To provide a higher level of detail, the data within these tables are further described using accompanying information or “dimensions.” Dimensions can include a product ID and unit price for each transaction, for example, as well as the date on which the transaction took place. Dimensional data models are optimized for faster data retrieval, and they make it simpler to organize business information into easily understood categories.

Relational modeling

The relational model is one of the oldest and most commonly used structures in database modeling. Relational data models store data in fixed-format records and arrange it into tables organized by rows and columns. Most relational data models contain two main elements: measures and dimensions. Measures are numeric values — like sales revenue or the total number of transactions — that can be used for mathematical calculations. Dimensions can contain numbers or text and are used for descriptions rather than calculations.

As its name implies, relational modeling defines the relationships within the structure. These relationships are essentially common data elements that link tables together.

Relational databases offer several distinct advantages. They can be easily queried and allow for the use of stored procedures to manipulate data. However, these models can struggle with larger amounts of data, leading to a decline in performance.

Entity-relationship modeling 

Entity-relationship (E-R) data modeling uses diagrams to depict a business’s data structure in graphical form. E-R models contain boxes of different shapes that represent entities and lines connecting them that represent relationships between entities, such as associations and dependencies. Similar to other techniques, E-R data modeling uses key elements to link tables together.

Careers you can pursue with data modeling skills 

As more businesses across industries recognize the importance of database architecture, more people are needed who can successfully engage data modeling. For those with a strong skill set in data modeling, the following tech careers are an opportunity to showcase their talent.

Data analyst 

Data analysts perform a range of duties that directly relate to data modeling, including gathering and analyzing data, identifying and interpreting trends in data, and implementing and maintaining databases for their employers. Their work helps organizations identify new ways to engage their customers, solve problems and improve the efficiency of operations.

Database architect 

Database architects are in charge of creating and organizing systems that store and secure data. They are commonly employed by companies that offer computer design or network security services, but these professionals can work in any industry that manages large amounts of data.

Database architects perform a variety of tasks that require data modeling skills, such as reviewing and enhancing an organization’s existing data infrastructure, designing and planning new databases, and overseeing the implementation of data storage systems and solutions.

Research scientist  

Computer and information research scientists are generally tasked with designing and developing new uses for existing computing technology. Their responsibilities can include evaluating their organization’s computing needs, developing new software systems that solve computing problems and improve operations, and conducting experiments to test the efficacy of their software systems. 

Because research scientists are frequently asked to develop and test new software systems, data modeling skills are extremely important for this role. Other valuable skills for computer and information research scientists include math, data analysis, data visualization, cloud computing and programming.

What type of degree do you need for a career in data modeling?

If you’re thinking of pursuing a career in data modeling, it’s important to consider the type of education you’ll need. There are several technology degree paths that can prepare you for a career in data modeling. The optimal path for you depends on your desired role and the qualifications you currently possess. 

Many entry-level data research and analysis jobs require at least a Bachelor of Science in Computer Science or a related field like information systems. However, some employers seek candidates with more specialized credentials that signify their data management and analysis skills, such as a Bachelor of Science in Data Science. These programs can provide a better foundation for a data modeling career as they focus heavily on the critical elements of database design.

There are a few emerging certifications in this area to be mindful of. CompTIA recently released a Data+ certification. IBM, Google and Microsoft also have certifications to consider if you’re looking to show off further education in your field on your resumé.

To qualify for a higher level or managerial role at most organizations, such as database architect or administrator, you may need to earn a more advanced degree, such as a Master of Science in Data Science or Information Systems. Even if it’s not listed as a requirement, an advanced degree can help you showcase your data management skills and stand out to employers on the job market.

Data science and IT programs at University of Phoenix

If you’re interested in learning fundamental and basic skills involving data, University of Phoenix offers online degrees in data science, information technology and computer science.

Bachelor of Science in Computer Science — This program equips you with the knowledge to apply information technology theory and principles to address real-world business challenges with advanced concepts in math, programming and computer architecture. You can also use elective courses to earn a certificate in cybersecurity, networking, cloud computing and much more.

Bachelor of Science in Information Technology — University of Phoenix worked with labor market researchers to align in-demand skills with its program to ensure students have the opportunity to learn and demonstrate them in their coursework. Learn skills pertaining to information systems, system analysis, operations and cybersecurity.

Bachelor of Science in Data Science — Gain fundamental skills and knowledge for analyzing, manipulating and processing data sets using statistical software. Learn ETL (extract, transform, load) processes for integrating data sets for business intelligence. Focus on data mining and modeling, data programming languages, statistical analysis, and data visualization and storytelling. Discover techniques to transform structured and unstructured data sets into meaningful information to identify data patterns and trends and drive strategic decision-making.

Master of Science in Data Science — In this program, you will learn how to analyze, design and manage data sets and models used to optimize functionality and scalability and improve business system performance. Learn database design, data processing and warehousing, data queries and interpretation, business intelligence and statistical methods, as well as how to apply data science strategically to improve business decision-making.

Headshot of Michael Feder


A graduate of Johns Hopkins University and its Writing Seminars program and winner of the Stephen A. Dixon Literary Prize, Michael Feder brings an eye for detail and a passion for research to every article he writes. His academic and professional background includes experience in marketing, content development, script writing and SEO. Today, he works as a multimedia specialist at University of Phoenix where he covers a variety of topics ranging from healthcare to IT.

Headshot of Kathryn Uhles


Currently Dean of the College of Business and Information Technology, Kathryn Uhles has served University of Phoenix in a variety of roles since 2006. Prior to joining University of Phoenix, Kathryn taught fifth grade to underprivileged youth in Phoenix.


This article has been vetted by University of Phoenix's editorial advisory committee. 
Read more about our editorial process.


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